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QuickStart
==========

Let's take a look at some code examples.

Matrix Operations
-----------------

Making use of SystemDS, let us multiply an Matrix with an scalar:

.. code-block:: python

  # Import SystemDSContext
  from systemds.context import SystemDSContext
  # Create a context and if necessary (no SystemDS py4j instance running)
  # it starts a subprocess which does the execution in SystemDS
  with SystemDSContext() as sds:
      # Full generates a matrix completely filled with one number.
      # Generate a 5x10 matrix filled with 4.2
      m = sds.full((5, 10), 4.20)
      # multiply with scalar. Nothing is executed yet!
      m_res = m * 3.1
      # Do the calculation in SystemDS by calling compute().
      # The returned value is an numpy array that can be directly printed.
      print(m_res.compute())
  # context will automatically be closed and process stopped

As output we get

.. code-block:: python

  [[ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
   [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
   [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
   [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
   [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]]

The Python SystemDS package is compatible with numpy arrays.
Let us do a quick element-wise matrix multiplication of numpy arrays with SystemDS.
Remember to first start up a new terminal:

.. code-block:: python

  import numpy as np  # import numpy

  # Import SystemDSContext
  from systemds.context import SystemDSContext

  # create a random array
  m1 = np.array(np.random.randint(100, size=5 * 5) + 1.01, dtype=np.double)
  m1.shape = (5, 5)
  # create another random array
  m2 = np.array(np.random.randint(5, size=5 * 5) + 1, dtype=np.double)
  m2.shape = (5, 5)

  # Create a context
  with SystemDSContext() as sds:
      # element-wise matrix multiplication, note that nothing is executed yet!
      m_res = sds.from_numpy(m1) * sds.from_numpy(m2)
      # lets do the actual computation in SystemDS! The result is an numpy array
      m_res_np = m_res.compute()
      print(m_res_np)

More complex operations
-----------------------

SystemDS provides algorithm level functions as built-in functions to simplify development.
One example of this is l2SVM, a high level functions for Data-Scientists. Let's take a look at l2svm:

.. code-block:: python

  # Import numpy and SystemDS
  import numpy as np
  from systemds.context import SystemDSContext
  from systemds.operator.algorithm import l2svm

  # Set a seed
  np.random.seed(0)
  # Generate random features and labels in numpy
  # This can easily be exchanged with a data set.
  features = np.array(np.random.randint(100, size=10 * 10) + 1.01, dtype=np.double)
  features.shape = (10, 10)
  labels = np.zeros((10, 1))

  # l2svm labels can only be 0 or 1
  for i in range(10):
      if np.random.random() > 0.5:
          labels[i][0] = 1

  # compute our model
  with SystemDSContext() as sds:
      model = l2svm(sds.from_numpy(features), sds.from_numpy(labels)).compute()
      print(model)

The output should be similar to

.. code-block:: python

  [[ 0.02033445]
   [-0.00324092]
   [ 0.0014692 ]
   [ 0.02649209]
   [-0.00616902]
   [-0.0095087 ]
   [ 0.01039221]
   [-0.0011352 ]
   [-0.01686351]
   [-0.03839821]]

